{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PIMA Model using PYCaret 1.0"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Context: - \n",
    "\n",
    "This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.\n",
    "\n",
    "\n",
    "Business Problem: - \n",
    "Build a machine learning model to accurately predict whether or not the patients in the dataset have diabetes or not?\n",
    "\n",
    "Acknowledgements\n",
    "Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., & Johannes, R.S. (1988). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Symposium on Computer Applications and Medical Care (pp. 261--265). IEEE Computer Society Press.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('datasets_228_482_diabetes.csv')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Peeking into data we see our target is Outcome 1- Diabetic, 0-Not diabetic. All numeric data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
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       "      <td>64</td>\n",
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       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
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       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape # we have  768 rows and 9 data columns  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import plotly.offline as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "import plotly.tools as tls\n",
    "import plotly.figure_factory as ff\n",
    "py.init_notebook_mode(connected=True)\n",
    "import squarify\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "Diabetic = dataset[(dataset['Outcome'] != 0)]\n",
    "Non_diabetic = dataset[(dataset['Outcome'] == 0)]\n",
    "\n",
    "\n",
    "def target_count():\n",
    "    trace = go.Bar( x = dataset['Outcome'].value_counts().values.tolist(), \n",
    "                    y = ['Non_diabetic','diabetic' ], \n",
    "                    orientation = 'h', \n",
    "                    text=dataset['Outcome'].value_counts().values.tolist(), \n",
    "                    textfont=dict(size=15),\n",
    "                    textposition = 'auto',\n",
    "                    opacity = 0.8,marker=dict(\n",
    "                    color=['lightskyblue', 'gold'],\n",
    "                    line=dict(color='#000000',width=1.5)))\n",
    "\n",
    "    layout = dict(title =  'Outcome variable')\n",
    "\n",
    "    fig = dict(data = [trace], layout=layout)\n",
    "    py.iplot(fig)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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        "plotlyServerURL": "https://plot.ly",
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         "textfont": {
          "size": 15
         },
         "textposition": "auto",
         "type": "bar",
         "x": [
          500,
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         "y": [
          "Non_diabetic",
          "diabetic"
         ]
        }
       ],
       "layout": {
        "template": {
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           {
            "error_x": {
             "color": "#2a3f5f"
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   "source": [
    "Dataset is clearly unbalanced we can use SMOTE sampling to balance the classes. But In this notebook we are not going to look at it. "
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  {
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   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visulazing the distibution of the data for every feature\n",
    "plt.figure(figsize=(20, 20))\n",
    "\n",
    "for i, column in enumerate(dataset.columns, 1):\n",
    "    plt.subplot(3, 3, i)\n",
    "    dataset[dataset[\"Outcome\"] == 0][column].hist(bins=35, color='blue', label='Have Diabetes = NO', alpha=0.6)\n",
    "    dataset[dataset[\"Outcome\"] == 1][column].hist(bins=35, color='yellow', label='Have Diabetes = YES', alpha=0.6)\n",
    "    plt.legend()\n",
    "    plt.xlabel(column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before doing anything the first thing we need to do is splitting data into training set and validation set. \n",
    "1)\tTraining set : - Data set on which we build the model  and fine tune the model. \n",
    "2)\tValidation set: - Data set on which we test how well our finalized model is performing. It is important that during the modelling stage we don’t expose this data for our model. This should be unseen instance for our model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for Modeling: (730, 9)\n",
      "Unseen Data For Predictions (38, 9)\n"
     ]
    }
   ],
   "source": [
    "data = dataset.sample(frac=0.95, random_state=786)\n",
    "data_unseen = dataset.drop(data.index).reset_index(drop=True)\n",
    "data.reset_index(drop=True, inplace=True)\n",
    "\n",
    "print('Data for Modeling: ' + str(data.shape))\n",
    "print('Unseen Data For Predictions ' + str(data_unseen.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have 730 data points to model and 38 data points to test my model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.classification import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup function in PyCaret is the most important function this is where we perform all our data preprocessing steps. \n",
    "•\tData = Data for modelling \n",
    "•\tTarget = Target column that we want to predict in this case it is diabetic or not \n",
    "•\tSession_id  = User defined session id \n",
    "•\tNormalization =  Machine learning models work well when the input features do not have huge variation such as BMI and  Glucose their values are on different scale. It is important to scale then hence we use normalize parameter \n",
    "•\tTransformation = While normalization reduces the variance transformation changes the data so that It could be represented in Gaussian distribution (normal curve).\n",
    "•\tMulticollinearity: - When the data  is highly co-related our algorithms tend not to generalize very well so it is important to remove multi- collinearity by using the remove_multicollinearity  and multicollinearity_threshold parameters in setup\n",
    "•\tSometimes a dataset may have a categorical feature with multiple levels, where distribution of such levels are skewed and one level may dominate over other levels. This means there is not much variation in the information provided by such feature.  For a ML model, such feature may not add a lot of information and thus can be ignored for modeling. This can be achieved in PyCaret using ignore_low_variance parameter \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
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       "        }</style><table id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row0_col1\" class=\"data row0 col1\" >1229</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row1_col0\" class=\"data row1 col0\" >Target Type</td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row1_col1\" class=\"data row1 col1\" >Binary</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row2_col0\" class=\"data row2 col0\" >Label Encoded</td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row2_col1\" class=\"data row2 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row3_col1\" class=\"data row3 col1\" >(730, 9)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row5_col1\" class=\"data row5 col1\" >7</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row6_col1\" class=\"data row6 col1\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row10_col1\" class=\"data row10 col1\" >(730, 9)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row11_col1\" class=\"data row11 col1\" >(510, 22)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row12_col1\" class=\"data row12 col1\" >(220, 22)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row15_col1\" class=\"data row15 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row16_col1\" class=\"data row16 col1\" >zscore</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row17_col1\" class=\"data row17 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row18_col1\" class=\"data row18 col1\" >yeo-johnson</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row22_col1\" class=\"data row22 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row28_col1\" class=\"data row28 col1\" >True</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row29_col1\" class=\"data row29 col1\" >0.95</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row39_col1\" class=\"data row39 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_21d98976_d5d1_11ea_a8cf_b0521692a616row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x28876ef2438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = setup(data = data, target = 'Outcome',session_id=1229,normalize=True,transformation=True,ignore_low_variance=True,\n",
    "           remove_multicollinearity=True, multicollinearity_threshold=0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before you proceed Make sure all your data types are inferred correctly If so press enter if not change the data types. You can find more info about data types that on this page https://pycaret.org/data-types/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
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       "        }</style><table id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >Accuracy</th>        <th class=\"col_heading level0 col2\" >AUC</th>        <th class=\"col_heading level0 col3\" >Recall</th>        <th class=\"col_heading level0 col4\" >Prec.</th>        <th class=\"col_heading level0 col5\" >F1</th>        <th class=\"col_heading level0 col6\" >Kappa</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row0_col0\" class=\"data row0 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row0_col1\" class=\"data row0 col1\" >0.7471</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row0_col2\" class=\"data row0 col2\" >0.8286</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row0_col3\" class=\"data row0 col3\" >0.5922</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row0_col4\" class=\"data row0 col4\" >0.6589</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row0_col5\" class=\"data row0 col5\" >0.6168</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row0_col6\" class=\"data row0 col6\" >0.4297</td>\n",
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       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row1_col0\" class=\"data row1 col0\" >Gradient Boosting Classifier</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row1_col1\" class=\"data row1 col1\" >0.751</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row1_col2\" class=\"data row1 col2\" >0.826</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row1_col3\" class=\"data row1 col3\" >0.5859</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row1_col4\" class=\"data row1 col4\" >0.6737</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row1_col5\" class=\"data row1 col5\" >0.6134</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row1_col6\" class=\"data row1 col6\" >0.433</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row2_col0\" class=\"data row2 col0\" >CatBoost Classifier</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row2_col1\" class=\"data row2 col1\" >0.7745</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row2_col2\" class=\"data row2 col2\" >0.8227</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row2_col3\" class=\"data row2 col3\" >0.6101</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row2_col4\" class=\"data row2 col4\" >0.7165</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row2_col5\" class=\"data row2 col5\" >0.6546</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row2_col6\" class=\"data row2 col6\" >0.4886</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row3_col0\" class=\"data row3 col0\" >Logistic Regression</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row3_col1\" class=\"data row3 col1\" >0.7451</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row3_col2\" class=\"data row3 col2\" >0.8145</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row3_col3\" class=\"data row3 col3\" >0.5605</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row3_col4\" class=\"data row3 col4\" >0.6661</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row3_col5\" class=\"data row3 col5\" >0.605</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row3_col6\" class=\"data row3 col6\" >0.4192</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row4_col0\" class=\"data row4 col0\" >Linear Discriminant Analysis</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row4_col1\" class=\"data row4 col1\" >0.7392</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row4_col3\" class=\"data row4 col3\" >0.5546</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row4_col4\" class=\"data row4 col4\" >0.6513</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row4_col5\" class=\"data row4 col5\" >0.5951</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row4_col6\" class=\"data row4 col6\" >0.4053</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row5_col0\" class=\"data row5 col0\" >Extra Trees Classifier</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row5_col1\" class=\"data row5 col1\" >0.7431</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row5_col2\" class=\"data row5 col2\" >0.8087</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row5_col3\" class=\"data row5 col3\" >0.4977</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row5_col4\" class=\"data row5 col4\" >0.6888</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row5_col5\" class=\"data row5 col5\" >0.5718</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row5_col6\" class=\"data row5 col6\" >0.3958</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row6_col0\" class=\"data row6 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row6_col1\" class=\"data row6 col1\" >0.7314</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row6_col2\" class=\"data row6 col2\" >0.8031</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row6_col3\" class=\"data row6 col3\" >0.5477</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row6_col4\" class=\"data row6 col4\" >0.6418</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row6_col5\" class=\"data row6 col5\" >0.5852</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row6_col6\" class=\"data row6 col6\" >0.3887</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row7_col0\" class=\"data row7 col0\" >Ada Boost Classifier</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row7_col1\" class=\"data row7 col1\" >0.7392</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row7_col2\" class=\"data row7 col2\" >0.7952</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row7_col3\" class=\"data row7 col3\" >0.5536</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row7_col4\" class=\"data row7 col4\" >0.6519</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row7_col5\" class=\"data row7 col5\" >0.5954</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row7_col6\" class=\"data row7 col6\" >0.4052</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row8_col0\" class=\"data row8 col0\" >Random Forest Classifier</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row8_col1\" class=\"data row8 col1\" >0.7275</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row8_col2\" class=\"data row8 col2\" >0.7703</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row8_col3\" class=\"data row8 col3\" >0.4618</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row8_col4\" class=\"data row8 col4\" >0.6573</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row8_col5\" class=\"data row8 col5\" >0.5346</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row8_col6\" class=\"data row8 col6\" >0.3522</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row9_col0\" class=\"data row9 col0\" >K Neighbors Classifier</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row9_col1\" class=\"data row9 col1\" >0.7157</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row9_col2\" class=\"data row9 col2\" >0.7637</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row9_col3\" class=\"data row9 col3\" >0.5297</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row9_col4\" class=\"data row9 col4\" >0.6222</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row9_col5\" class=\"data row9 col5\" >0.5597</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row9_col6\" class=\"data row9 col6\" >0.354</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row10_col0\" class=\"data row10 col0\" >Naive Bayes</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row10_col1\" class=\"data row10 col1\" >0.7</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row10_col2\" class=\"data row10 col2\" >0.7491</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row10_col3\" class=\"data row10 col3\" >0.2869</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row10_col4\" class=\"data row10 col4\" >0.6575</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row10_col5\" class=\"data row10 col5\" >0.3886</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row10_col6\" class=\"data row10 col6\" >0.2347</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row11_col0\" class=\"data row11 col0\" >Decision Tree Classifier</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row11_col1\" class=\"data row11 col1\" >0.6765</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row11_col2\" class=\"data row11 col2\" >0.6421</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row11_col3\" class=\"data row11 col3\" >0.531</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row11_col4\" class=\"data row11 col4\" >0.5373</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row11_col5\" class=\"data row11 col5\" >0.5283</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row11_col6\" class=\"data row11 col6\" >0.2841</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row12_col0\" class=\"data row12 col0\" >Quadratic Discriminant Analysis</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row12_col1\" class=\"data row12 col1\" >0.5784</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row12_col2\" class=\"data row12 col2\" >0.6326</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row12_col3\" class=\"data row12 col3\" >0.3899</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row12_col4\" class=\"data row12 col4\" >0.3535</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row12_col5\" class=\"data row12 col5\" >0.3177</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row12_col6\" class=\"data row12 col6\" >0.0644</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row13_col0\" class=\"data row13 col0\" >SVM - Linear Kernel</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row13_col1\" class=\"data row13 col1\" >0.7039</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row13_col2\" class=\"data row13 col2\" >0</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row13_col3\" class=\"data row13 col3\" >0.5546</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row13_col4\" class=\"data row13 col4\" >0.6123</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row13_col5\" class=\"data row13 col5\" >0.5524</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row13_col6\" class=\"data row13 col6\" >0.3389</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row14_col0\" class=\"data row14 col0\" >Ridge Classifier</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row14_col1\" class=\"data row14 col1\" >0.7451</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row14_col2\" class=\"data row14 col2\" >0</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row14_col3\" class=\"data row14 col3\" >0.566</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row14_col4\" class=\"data row14 col4\" >0.6611</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row14_col5\" class=\"data row14 col5\" >0.6057</td>\n",
       "                        <td id=\"T_2182ff6e_d5d2_11ea_8969_b0521692a616row14_col6\" class=\"data row14 col6\" >0.4197</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x288774feac8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_models(sort='AUC')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "In one line Pycaret fitted 14 models. I am using sort parameter and sorting by AUC because I am interested in optimizing AUC as my classifier metric. It is important to me that my classifier differentiates 1’s as 1’s and 0’s as 0’s. We can sort it by any metric choosing your metric depends on your business case. \n",
    "\n",
    "Here I am picking CatBoost Classifer because it fared well with all metrics except AUC let's see if we can optimize it.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Prec.</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.8039</td>\n",
       "      <td>0.8426</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.7692</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.5312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.8431</td>\n",
       "      <td>0.8651</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.9091</td>\n",
       "      <td>0.7143</td>\n",
       "      <td>0.6129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.8039</td>\n",
       "      <td>0.8443</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.7333</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.5455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.8586</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.4848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.7843</td>\n",
       "      <td>0.7845</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.7059</td>\n",
       "      <td>0.6857</td>\n",
       "      <td>0.5217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.8039</td>\n",
       "      <td>0.8485</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.8333</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.5355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.6863</td>\n",
       "      <td>0.7424</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.3131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.8081</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.3990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.8249</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.7143</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.4574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.8081</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.4848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mean</td>\n",
       "      <td>0.7745</td>\n",
       "      <td>0.8227</td>\n",
       "      <td>0.6101</td>\n",
       "      <td>0.7165</td>\n",
       "      <td>0.6546</td>\n",
       "      <td>0.4886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SD</td>\n",
       "      <td>0.0423</td>\n",
       "      <td>0.0360</td>\n",
       "      <td>0.0457</td>\n",
       "      <td>0.0979</td>\n",
       "      <td>0.0433</td>\n",
       "      <td>0.0798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Accuracy     AUC  Recall   Prec.      F1   Kappa\n",
       "0       0.8039  0.8426  0.5882  0.7692  0.6667  0.5312\n",
       "1       0.8431  0.8651  0.5882  0.9091  0.7143  0.6129\n",
       "2       0.8039  0.8443  0.6471  0.7333  0.6875  0.5455\n",
       "3       0.7647  0.8586  0.6667  0.6667  0.6667  0.4848\n",
       "4       0.7843  0.7845  0.6667  0.7059  0.6857  0.5217\n",
       "5       0.8039  0.8485  0.5556  0.8333  0.6667  0.5355\n",
       "6       0.6863  0.7424  0.5556  0.5556  0.5556  0.3131\n",
       "7       0.7255  0.8081  0.6111  0.6111  0.6111  0.3990\n",
       "8       0.7647  0.8249  0.5556  0.7143  0.6250  0.4574\n",
       "9       0.7647  0.8081  0.6667  0.6667  0.6667  0.4848\n",
       "Mean    0.7745  0.8227  0.6101  0.7165  0.6546  0.4886\n",
       "SD      0.0423  0.0360  0.0457  0.0979  0.0433  0.0798"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "catboost = create_model('catboost',fold =10) #CatBoost Classifier"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "catboost is giving us  82.27  AUC let’s see if we can improve on that by using tune_model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Prec.</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.8806</td>\n",
       "      <td>0.5294</td>\n",
       "      <td>0.6923</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.4375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.8431</td>\n",
       "      <td>0.8910</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.9091</td>\n",
       "      <td>0.7143</td>\n",
       "      <td>0.6129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.8235</td>\n",
       "      <td>0.8720</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.8333</td>\n",
       "      <td>0.6897</td>\n",
       "      <td>0.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.8039</td>\n",
       "      <td>0.8956</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.7857</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.5479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.7744</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.3990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.7451</td>\n",
       "      <td>0.8266</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>0.7273</td>\n",
       "      <td>0.5517</td>\n",
       "      <td>0.3878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.7037</td>\n",
       "      <td>0.3889</td>\n",
       "      <td>0.4118</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.0870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.8283</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.7143</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.4574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.8030</td>\n",
       "      <td>0.3333</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.7451</td>\n",
       "      <td>0.7946</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.6286</td>\n",
       "      <td>0.4348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mean</td>\n",
       "      <td>0.7451</td>\n",
       "      <td>0.8270</td>\n",
       "      <td>0.5261</td>\n",
       "      <td>0.6832</td>\n",
       "      <td>0.5908</td>\n",
       "      <td>0.4100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SD</td>\n",
       "      <td>0.0739</td>\n",
       "      <td>0.0577</td>\n",
       "      <td>0.0964</td>\n",
       "      <td>0.1417</td>\n",
       "      <td>0.1057</td>\n",
       "      <td>0.1598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Accuracy     AUC  Recall   Prec.      F1   Kappa\n",
       "0       0.7647  0.8806  0.5294  0.6923  0.6000  0.4375\n",
       "1       0.8431  0.8910  0.5882  0.9091  0.7143  0.6129\n",
       "2       0.8235  0.8720  0.5882  0.8333  0.6897  0.5714\n",
       "3       0.8039  0.8956  0.6111  0.7857  0.6875  0.5479\n",
       "4       0.7255  0.7744  0.6111  0.6111  0.6111  0.3990\n",
       "5       0.7451  0.8266  0.4444  0.7273  0.5517  0.3878\n",
       "6       0.5882  0.7037  0.3889  0.4118  0.4000  0.0870\n",
       "7       0.7647  0.8283  0.5556  0.7143  0.6250  0.4574\n",
       "8       0.6471  0.8030  0.3333  0.5000  0.4000  0.1639\n",
       "9       0.7451  0.7946  0.6111  0.6471  0.6286  0.4348\n",
       "Mean    0.7451  0.8270  0.5261  0.6832  0.5908  0.4100\n",
       "SD      0.0739  0.0577  0.0964  0.1417  0.1057  0.1598"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_cat_boost= tune_model('catboost', optimize = 'AUC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well we have improved from 82.27 tp 82.70 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets Create  more classifiers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Prec.</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.8235</td>\n",
       "      <td>0.8685</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.7857</td>\n",
       "      <td>0.7097</td>\n",
       "      <td>0.5846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.8235</td>\n",
       "      <td>0.8858</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.8333</td>\n",
       "      <td>0.6897</td>\n",
       "      <td>0.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.7843</td>\n",
       "      <td>0.8772</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.5075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.8283</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.3834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.7059</td>\n",
       "      <td>0.7290</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.5714</td>\n",
       "      <td>0.3478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.7451</td>\n",
       "      <td>0.8098</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>0.7273</td>\n",
       "      <td>0.5517</td>\n",
       "      <td>0.3878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.6078</td>\n",
       "      <td>0.7020</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>0.1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.7744</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.6429</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.3670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.8098</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.6429</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.3670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.7843</td>\n",
       "      <td>0.8603</td>\n",
       "      <td>0.7222</td>\n",
       "      <td>0.6842</td>\n",
       "      <td>0.7027</td>\n",
       "      <td>0.5337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mean</td>\n",
       "      <td>0.7451</td>\n",
       "      <td>0.8145</td>\n",
       "      <td>0.5605</td>\n",
       "      <td>0.6661</td>\n",
       "      <td>0.6050</td>\n",
       "      <td>0.4192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SD</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>0.0599</td>\n",
       "      <td>0.0873</td>\n",
       "      <td>0.1024</td>\n",
       "      <td>0.0807</td>\n",
       "      <td>0.1270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Accuracy     AUC  Recall   Prec.      F1   Kappa\n",
       "0       0.8235  0.8685  0.6471  0.7857  0.7097  0.5846\n",
       "1       0.8235  0.8858  0.5882  0.8333  0.6897  0.5714\n",
       "2       0.7843  0.8772  0.6471  0.6875  0.6667  0.5075\n",
       "3       0.7255  0.8283  0.5556  0.6250  0.5882  0.3834\n",
       "4       0.7059  0.7290  0.5556  0.5882  0.5714  0.3478\n",
       "5       0.7451  0.8098  0.4444  0.7273  0.5517  0.3878\n",
       "6       0.6078  0.7020  0.4444  0.4444  0.4444  0.1414\n",
       "7       0.7255  0.7744  0.5000  0.6429  0.5625  0.3670\n",
       "8       0.7255  0.8098  0.5000  0.6429  0.5625  0.3670\n",
       "9       0.7843  0.8603  0.7222  0.6842  0.7027  0.5337\n",
       "Mean    0.7451  0.8145  0.5605  0.6661  0.6050  0.4192\n",
       "SD      0.0608  0.0599  0.0873  0.1024  0.0807  0.1270"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr = create_model('lr', fold =10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Prec.</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.8235</td>\n",
       "      <td>0.8702</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.7857</td>\n",
       "      <td>0.7097</td>\n",
       "      <td>0.5846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.8235</td>\n",
       "      <td>0.8858</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.8333</td>\n",
       "      <td>0.6897</td>\n",
       "      <td>0.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.8039</td>\n",
       "      <td>0.8841</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.7333</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.5455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.8367</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.3834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.7059</td>\n",
       "      <td>0.7290</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.5714</td>\n",
       "      <td>0.3478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.8098</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.5333</td>\n",
       "      <td>0.3497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.6078</td>\n",
       "      <td>0.7037</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>0.1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.7778</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.6429</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.3670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.8081</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.6429</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.3670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.7843</td>\n",
       "      <td>0.8586</td>\n",
       "      <td>0.7222</td>\n",
       "      <td>0.6842</td>\n",
       "      <td>0.7027</td>\n",
       "      <td>0.5337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mean</td>\n",
       "      <td>0.7451</td>\n",
       "      <td>0.8164</td>\n",
       "      <td>0.5605</td>\n",
       "      <td>0.6647</td>\n",
       "      <td>0.6052</td>\n",
       "      <td>0.4192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SD</td>\n",
       "      <td>0.0626</td>\n",
       "      <td>0.0604</td>\n",
       "      <td>0.0873</td>\n",
       "      <td>0.1025</td>\n",
       "      <td>0.0839</td>\n",
       "      <td>0.1317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Accuracy     AUC  Recall   Prec.      F1   Kappa\n",
       "0       0.8235  0.8702  0.6471  0.7857  0.7097  0.5846\n",
       "1       0.8235  0.8858  0.5882  0.8333  0.6897  0.5714\n",
       "2       0.8039  0.8841  0.6471  0.7333  0.6875  0.5455\n",
       "3       0.7255  0.8367  0.5556  0.6250  0.5882  0.3834\n",
       "4       0.7059  0.7290  0.5556  0.5882  0.5714  0.3478\n",
       "5       0.7255  0.8098  0.4444  0.6667  0.5333  0.3497\n",
       "6       0.6078  0.7037  0.4444  0.4444  0.4444  0.1414\n",
       "7       0.7255  0.7778  0.5000  0.6429  0.5625  0.3670\n",
       "8       0.7255  0.8081  0.5000  0.6429  0.5625  0.3670\n",
       "9       0.7843  0.8586  0.7222  0.6842  0.7027  0.5337\n",
       "Mean    0.7451  0.8164  0.5605  0.6647  0.6052  0.4192\n",
       "SD      0.0626  0.0604  0.0873  0.1025  0.0839  0.1317"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_lr= tune_model('lr',optimize='AUC') # tuned_logistic   81.6 AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Prec.</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.7451</td>\n",
       "      <td>0.8356</td>\n",
       "      <td>0.4118</td>\n",
       "      <td>0.7000</td>\n",
       "      <td>0.5185</td>\n",
       "      <td>0.3607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.8339</td>\n",
       "      <td>0.3529</td>\n",
       "      <td>0.8571</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.3793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.8235</td>\n",
       "      <td>0.8564</td>\n",
       "      <td>0.7059</td>\n",
       "      <td>0.7500</td>\n",
       "      <td>0.7273</td>\n",
       "      <td>0.5970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.8235</td>\n",
       "      <td>0.8687</td>\n",
       "      <td>0.7778</td>\n",
       "      <td>0.7368</td>\n",
       "      <td>0.7568</td>\n",
       "      <td>0.6185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.7761</td>\n",
       "      <td>0.7222</td>\n",
       "      <td>0.5909</td>\n",
       "      <td>0.6500</td>\n",
       "      <td>0.4279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.8670</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.4715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.7391</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.4706</td>\n",
       "      <td>0.2073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.8039</td>\n",
       "      <td>0.8502</td>\n",
       "      <td>0.7222</td>\n",
       "      <td>0.7222</td>\n",
       "      <td>0.7222</td>\n",
       "      <td>0.5707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.8401</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.6429</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.3670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.6863</td>\n",
       "      <td>0.7929</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>0.5789</td>\n",
       "      <td>0.3300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mean</td>\n",
       "      <td>0.7510</td>\n",
       "      <td>0.8260</td>\n",
       "      <td>0.5859</td>\n",
       "      <td>0.6737</td>\n",
       "      <td>0.6134</td>\n",
       "      <td>0.4330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SD</td>\n",
       "      <td>0.0548</td>\n",
       "      <td>0.0406</td>\n",
       "      <td>0.1420</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.0969</td>\n",
       "      <td>0.1248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Accuracy     AUC  Recall   Prec.      F1   Kappa\n",
       "0       0.7451  0.8356  0.4118  0.7000  0.5185  0.3607\n",
       "1       0.7647  0.8339  0.3529  0.8571  0.5000  0.3793\n",
       "2       0.8235  0.8564  0.7059  0.7500  0.7273  0.5970\n",
       "3       0.8235  0.8687  0.7778  0.7368  0.7568  0.6185\n",
       "4       0.7255  0.7761  0.7222  0.5909  0.6500  0.4279\n",
       "5       0.7647  0.8670  0.6111  0.6875  0.6471  0.4715\n",
       "6       0.6471  0.7391  0.4444  0.5000  0.4706  0.2073\n",
       "7       0.8039  0.8502  0.7222  0.7222  0.7222  0.5707\n",
       "8       0.7255  0.8401  0.5000  0.6429  0.5625  0.3670\n",
       "9       0.6863  0.7929  0.6111  0.5500  0.5789  0.3300\n",
       "Mean    0.7510  0.8260  0.5859  0.6737  0.6134  0.4330\n",
       "SD      0.0548  0.0406  0.1420  0.1000  0.0969  0.1248"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbc= create_model('gbc',fold =10) #  Gradient boosting  82.60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Prec.</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.8322</td>\n",
       "      <td>0.4118</td>\n",
       "      <td>0.6364</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.3226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.8028</td>\n",
       "      <td>0.4706</td>\n",
       "      <td>0.7273</td>\n",
       "      <td>0.5714</td>\n",
       "      <td>0.4194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.8806</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.4706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.7843</td>\n",
       "      <td>0.8855</td>\n",
       "      <td>0.7222</td>\n",
       "      <td>0.6842</td>\n",
       "      <td>0.7027</td>\n",
       "      <td>0.5337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.7451</td>\n",
       "      <td>0.7643</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.6286</td>\n",
       "      <td>0.4348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.8165</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.7143</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.4574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.6863</td>\n",
       "      <td>0.7104</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.3131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.6275</td>\n",
       "      <td>0.7441</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.4737</td>\n",
       "      <td>0.4865</td>\n",
       "      <td>0.1945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.8165</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.6429</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.3670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.7451</td>\n",
       "      <td>0.7997</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.6286</td>\n",
       "      <td>0.4348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mean</td>\n",
       "      <td>0.7333</td>\n",
       "      <td>0.8053</td>\n",
       "      <td>0.5585</td>\n",
       "      <td>0.6375</td>\n",
       "      <td>0.5908</td>\n",
       "      <td>0.3948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SD</td>\n",
       "      <td>0.0440</td>\n",
       "      <td>0.0525</td>\n",
       "      <td>0.0874</td>\n",
       "      <td>0.0707</td>\n",
       "      <td>0.0642</td>\n",
       "      <td>0.0925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Accuracy     AUC  Recall   Prec.      F1   Kappa\n",
       "0       0.7255  0.8322  0.4118  0.6364  0.5000  0.3226\n",
       "1       0.7647  0.8028  0.4706  0.7273  0.5714  0.4194\n",
       "2       0.7647  0.8806  0.6471  0.6471  0.6471  0.4706\n",
       "3       0.7843  0.8855  0.7222  0.6842  0.7027  0.5337\n",
       "4       0.7451  0.7643  0.6111  0.6471  0.6286  0.4348\n",
       "5       0.7647  0.8165  0.5556  0.7143  0.6250  0.4574\n",
       "6       0.6863  0.7104  0.5556  0.5556  0.5556  0.3131\n",
       "7       0.6275  0.7441  0.5000  0.4737  0.4865  0.1945\n",
       "8       0.7255  0.8165  0.5000  0.6429  0.5625  0.3670\n",
       "9       0.7451  0.7997  0.6111  0.6471  0.6286  0.4348\n",
       "Mean    0.7333  0.8053  0.5585  0.6375  0.5908  0.3948\n",
       "SD      0.0440  0.0525  0.0874  0.0707  0.0642  0.0925"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_graident_bosting = tune_model('gbc',optimize='AUC') # tuned Gradient boosting 80.5 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Prec.</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.8633</td>\n",
       "      <td>0.3529</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.4615</td>\n",
       "      <td>0.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.8235</td>\n",
       "      <td>0.8478</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.8333</td>\n",
       "      <td>0.6897</td>\n",
       "      <td>0.5714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.7843</td>\n",
       "      <td>0.8495</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.5075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.8235</td>\n",
       "      <td>0.8889</td>\n",
       "      <td>0.7222</td>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.7429</td>\n",
       "      <td>0.6087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.7761</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.6000</td>\n",
       "      <td>0.6316</td>\n",
       "      <td>0.4138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.7451</td>\n",
       "      <td>0.8468</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.6061</td>\n",
       "      <td>0.4199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.7542</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.2273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.7451</td>\n",
       "      <td>0.8215</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.6316</td>\n",
       "      <td>0.6486</td>\n",
       "      <td>0.4489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.7451</td>\n",
       "      <td>0.8367</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.6061</td>\n",
       "      <td>0.4199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.7059</td>\n",
       "      <td>0.8013</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.5714</td>\n",
       "      <td>0.6154</td>\n",
       "      <td>0.3796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mean</td>\n",
       "      <td>0.7471</td>\n",
       "      <td>0.8286</td>\n",
       "      <td>0.5922</td>\n",
       "      <td>0.6589</td>\n",
       "      <td>0.6168</td>\n",
       "      <td>0.4297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SD</td>\n",
       "      <td>0.0507</td>\n",
       "      <td>0.0389</td>\n",
       "      <td>0.1025</td>\n",
       "      <td>0.0893</td>\n",
       "      <td>0.0793</td>\n",
       "      <td>0.1092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Accuracy     AUC  Recall   Prec.      F1   Kappa\n",
       "0       0.7255  0.8633  0.3529  0.6667  0.4615  0.3000\n",
       "1       0.8235  0.8478  0.5882  0.8333  0.6897  0.5714\n",
       "2       0.7843  0.8495  0.6471  0.6875  0.6667  0.5075\n",
       "3       0.8235  0.8889  0.7222  0.7647  0.7429  0.6087\n",
       "4       0.7255  0.7761  0.6667  0.6000  0.6316  0.4138\n",
       "5       0.7451  0.8468  0.5556  0.6667  0.6061  0.4199\n",
       "6       0.6471  0.7542  0.5000  0.5000  0.5000  0.2273\n",
       "7       0.7451  0.8215  0.6667  0.6316  0.6486  0.4489\n",
       "8       0.7451  0.8367  0.5556  0.6667  0.6061  0.4199\n",
       "9       0.7059  0.8013  0.6667  0.5714  0.6154  0.3796\n",
       "Mean    0.7471  0.8286  0.5922  0.6589  0.6168  0.4297\n",
       "SD      0.0507  0.0389  0.1025  0.0893  0.0793  0.1092"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb = create_model('xgboost',fold =10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets blend this above models and see if we can beat 82.7 cat boost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Prec.</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.8039</td>\n",
       "      <td>0.8737</td>\n",
       "      <td>0.5294</td>\n",
       "      <td>0.8182</td>\n",
       "      <td>0.6429</td>\n",
       "      <td>0.5161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.8431</td>\n",
       "      <td>0.8772</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.9091</td>\n",
       "      <td>0.7143</td>\n",
       "      <td>0.6129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.7843</td>\n",
       "      <td>0.8616</td>\n",
       "      <td>0.5882</td>\n",
       "      <td>0.7143</td>\n",
       "      <td>0.6452</td>\n",
       "      <td>0.4923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.8235</td>\n",
       "      <td>0.8721</td>\n",
       "      <td>0.7778</td>\n",
       "      <td>0.7368</td>\n",
       "      <td>0.7568</td>\n",
       "      <td>0.6185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.6863</td>\n",
       "      <td>0.7727</td>\n",
       "      <td>0.6111</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>0.5789</td>\n",
       "      <td>0.3300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.8586</td>\n",
       "      <td>0.5556</td>\n",
       "      <td>0.7143</td>\n",
       "      <td>0.6250</td>\n",
       "      <td>0.4574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.6471</td>\n",
       "      <td>0.7525</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.4706</td>\n",
       "      <td>0.2073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.7647</td>\n",
       "      <td>0.8266</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.4848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.8350</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.6429</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>0.3670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.7059</td>\n",
       "      <td>0.8215</td>\n",
       "      <td>0.6667</td>\n",
       "      <td>0.5714</td>\n",
       "      <td>0.6154</td>\n",
       "      <td>0.3796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mean</td>\n",
       "      <td>0.7549</td>\n",
       "      <td>0.8352</td>\n",
       "      <td>0.5928</td>\n",
       "      <td>0.6824</td>\n",
       "      <td>0.6278</td>\n",
       "      <td>0.4466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SD</td>\n",
       "      <td>0.0596</td>\n",
       "      <td>0.0410</td>\n",
       "      <td>0.0903</td>\n",
       "      <td>0.1182</td>\n",
       "      <td>0.0759</td>\n",
       "      <td>0.1214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Accuracy     AUC  Recall   Prec.      F1   Kappa\n",
       "0       0.8039  0.8737  0.5294  0.8182  0.6429  0.5161\n",
       "1       0.8431  0.8772  0.5882  0.9091  0.7143  0.6129\n",
       "2       0.7843  0.8616  0.5882  0.7143  0.6452  0.4923\n",
       "3       0.8235  0.8721  0.7778  0.7368  0.7568  0.6185\n",
       "4       0.6863  0.7727  0.6111  0.5500  0.5789  0.3300\n",
       "5       0.7647  0.8586  0.5556  0.7143  0.6250  0.4574\n",
       "6       0.6471  0.7525  0.4444  0.5000  0.4706  0.2073\n",
       "7       0.7647  0.8266  0.6667  0.6667  0.6667  0.4848\n",
       "8       0.7255  0.8350  0.5000  0.6429  0.5625  0.3670\n",
       "9       0.7059  0.8215  0.6667  0.5714  0.6154  0.3796\n",
       "Mean    0.7549  0.8352  0.5928  0.6824  0.6278  0.4466\n",
       "SD      0.0596  0.0410  0.0903  0.1182  0.0759  0.1214"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blend_specific_soft = blend_models(estimator_list = [lr,gbc,xgb], method = 'soft')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we did --- 83.52 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(blend_specific_soft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Prec.</th>\n",
       "      <th>F1</th>\n",
       "      <th>Kappa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Voting Classifier</td>\n",
       "      <td>0.8045</td>\n",
       "      <td>0.8397</td>\n",
       "      <td>0.5921</td>\n",
       "      <td>0.7895</td>\n",
       "      <td>0.6767</td>\n",
       "      <td>0.5407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model  Accuracy     AUC  Recall   Prec.      F1   Kappa\n",
       "0  Voting Classifier    0.8045  0.8397  0.5921  0.7895  0.6767  0.5407"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# alright lets make some predictions \n",
    "predict_model(blend_specific_soft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finalizing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = finalize_model(blend_specific_soft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Moment of truth … let’s see how our classifier does if we can predict using the unseen data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.201</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0919</td>\n",
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       "      <td>1</td>\n",
       "      <td>146</td>\n",
       "      <td>56</td>\n",
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       "      <td>0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0.564</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5897</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>103</td>\n",
       "      <td>66</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>39.1</td>\n",
       "      <td>0.344</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "      <td>48</td>\n",
       "      <td>18</td>\n",
       "      <td>76</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0.323</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>107</td>\n",
       "      <td>74</td>\n",
       "      <td>30</td>\n",
       "      <td>100</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.404</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>170</td>\n",
       "      <td>64</td>\n",
       "      <td>37</td>\n",
       "      <td>225</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0.356</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>85</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.2</td>\n",
       "      <td>0.382</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>138</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.1</td>\n",
       "      <td>0.236</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>68</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0.787</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>179</td>\n",
       "      <td>95</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>34.2</td>\n",
       "      <td>0.164</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>134</td>\n",
       "      <td>80</td>\n",
       "      <td>37</td>\n",
       "      <td>370</td>\n",
       "      <td>46.2</td>\n",
       "      <td>0.238</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>52</td>\n",
       "      <td>57</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.677</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>106</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0.296</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>108</td>\n",
       "      <td>62</td>\n",
       "      <td>10</td>\n",
       "      <td>278</td>\n",
       "      <td>25.3</td>\n",
       "      <td>0.881</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>155</td>\n",
       "      <td>84</td>\n",
       "      <td>44</td>\n",
       "      <td>545</td>\n",
       "      <td>38.7</td>\n",
       "      <td>0.619</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>68</td>\n",
       "      <td>28</td>\n",
       "      <td>205</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.875</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>129</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>231</td>\n",
       "      <td>27.5</td>\n",
       "      <td>0.527</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>58</td>\n",
       "      <td>16</td>\n",
       "      <td>52</td>\n",
       "      <td>32.7</td>\n",
       "      <td>0.166</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>115</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.2</td>\n",
       "      <td>0.343</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>124</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>0.368</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>107</td>\n",
       "      <td>72</td>\n",
       "      <td>30</td>\n",
       "      <td>82</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0.821</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>136</td>\n",
       "      <td>84</td>\n",
       "      <td>41</td>\n",
       "      <td>88</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>123</td>\n",
       "      <td>48</td>\n",
       "      <td>32</td>\n",
       "      <td>165</td>\n",
       "      <td>42.1</td>\n",
       "      <td>0.520</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>49</td>\n",
       "      <td>579</td>\n",
       "      <td>42.4</td>\n",
       "      <td>0.702</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>108</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.955</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>14</td>\n",
       "      <td>175</td>\n",
       "      <td>62</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.212</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>68</td>\n",
       "      <td>42</td>\n",
       "      <td>250</td>\n",
       "      <td>42.3</td>\n",
       "      <td>0.365</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>86</td>\n",
       "      <td>66</td>\n",
       "      <td>52</td>\n",
       "      <td>65</td>\n",
       "      <td>41.3</td>\n",
       "      <td>0.917</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.601</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>143</td>\n",
       "      <td>86</td>\n",
       "      <td>30</td>\n",
       "      <td>330</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.892</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>120</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0.407</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>174</td>\n",
       "      <td>58</td>\n",
       "      <td>22</td>\n",
       "      <td>194</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.593</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>106</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0.207</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>154</td>\n",
       "      <td>78</td>\n",
       "      <td>30</td>\n",
       "      <td>100</td>\n",
       "      <td>30.9</td>\n",
       "      <td>0.164</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>84</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.433</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>130</td>\n",
       "      <td>78</td>\n",
       "      <td>23</td>\n",
       "      <td>79</td>\n",
       "      <td>28.4</td>\n",
       "      <td>0.323</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>106</td>\n",
       "      <td>56</td>\n",
       "      <td>27</td>\n",
       "      <td>165</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.426</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "      <td>88</td>\n",
       "      <td>39</td>\n",
       "      <td>110</td>\n",
       "      <td>36.5</td>\n",
       "      <td>1.057</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0             5      116             74              0        0  25.6   \n",
       "1             1      146             56              0        0  29.7   \n",
       "2             7      103             66             32        0  39.1   \n",
       "3             1       71             48             18       76  20.4   \n",
       "4             2      107             74             30      100  33.6   \n",
       "5             3      170             64             37      225  34.5   \n",
       "6             6       85             78              0        0  31.2   \n",
       "7             1      138             82              0        0  40.1   \n",
       "8             0      108             68             20        0  27.3   \n",
       "9             7      179             95             31        0  34.2   \n",
       "10            6      134             80             37      370  46.2   \n",
       "11            2      100             70             52       57  40.5   \n",
       "12            7      106             60             24        0  26.5   \n",
       "13            2      108             62             10      278  25.3   \n",
       "14            5      155             84             44      545  38.7   \n",
       "15            2      124             68             28      205  32.9   \n",
       "16            4      129             60             12      231  27.5   \n",
       "17            2       87             58             16       52  32.7   \n",
       "18            5      115             76              0        0  31.2   \n",
       "19            6      124             72              0        0  27.6   \n",
       "20            1      107             72             30       82  30.8   \n",
       "21            5      136             84             41       88  35.0   \n",
       "22            2      123             48             32      165  42.1   \n",
       "23            1      172             68             49      579  42.4   \n",
       "24            8      108             70              0        0  30.5   \n",
       "25           14      175             62             30        0  33.6   \n",
       "26            0      135             68             42      250  42.3   \n",
       "27            1       86             66             52       65  41.3   \n",
       "28            0       91             80              0        0  32.4   \n",
       "29            1      143             86             30      330  30.1   \n",
       "30            1      109             38             18      120  23.1   \n",
       "31            3      174             58             22      194  32.9   \n",
       "32            3      106             72              0        0  25.8   \n",
       "33            9      154             78             30      100  30.9   \n",
       "34            0      141             84             26        0  32.4   \n",
       "35            3      130             78             23       79  28.4   \n",
       "36            2      106             56             27      165  29.0   \n",
       "37            1      128             88             39      110  36.5   \n",
       "\n",
       "    DiabetesPedigreeFunction  Age  Outcome  Label   Score  \n",
       "0                      0.201   30        0      0  0.0919  \n",
       "1                      0.564   29        0      1  0.5897  \n",
       "2                      0.344   31        1      0  0.3279  \n",
       "3                      0.323   22        0      0  0.0089  \n",
       "4                      0.404   23        0      0  0.1317  \n",
       "5                      0.356   30        1      1  0.8249  \n",
       "6                      0.382   42        0      0  0.0963  \n",
       "7                      0.236   28        0      0  0.3645  \n",
       "8                      0.787   32        0      0  0.4141  \n",
       "9                      0.164   60        0      1  0.8250  \n",
       "10                     0.238   46        1      1  0.5383  \n",
       "11                     0.677   25        0      0  0.1914  \n",
       "12                     0.296   29        1      0  0.1062  \n",
       "13                     0.881   22        0      0  0.0408  \n",
       "14                     0.619   34        0      1  0.7832  \n",
       "15                     0.875   30        1      0  0.3710  \n",
       "16                     0.527   31        0      0  0.3712  \n",
       "17                     0.166   25        0      0  0.0352  \n",
       "18                     0.343   44        1      0  0.4372  \n",
       "19                     0.368   29        1      0  0.2061  \n",
       "20                     0.821   24        0      0  0.1376  \n",
       "21                     0.286   35        1      0  0.4070  \n",
       "22                     0.520   26        0      1  0.5091  \n",
       "23                     0.702   28        1      1  0.8388  \n",
       "24                     0.955   33        1      1  0.6104  \n",
       "25                     0.212   38        1      1  0.8887  \n",
       "26                     0.365   24        1      0  0.2821  \n",
       "27                     0.917   29        0      0  0.2166  \n",
       "28                     0.601   27        0      0  0.0911  \n",
       "29                     0.892   23        0      0  0.2742  \n",
       "30                     0.407   26        0      0  0.0626  \n",
       "31                     0.593   36        1      1  0.8884  \n",
       "32                     0.207   27        0      0  0.0575  \n",
       "33                     0.164   45        0      1  0.6396  \n",
       "34                     0.433   22        0      0  0.3546  \n",
       "35                     0.323   34        1      0  0.2527  \n",
       "36                     0.426   22        0      0  0.0768  \n",
       "37                     1.057   37        1      1  0.5804  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_predictions = predict_model(final_model, data=data_unseen)\n",
    "unseen_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Label and score are added to the data frame. \n",
    "•\tLabel is the predicted outcome \n",
    "•\tScore is the predicted probability \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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